Biblio
FPGAs are becoming a common sight in cloud environments and new usage paradigms, such as FPGA-as-a-Service, have emerged. This development poses a challenge to traditional FPGA security models, as these are assuming trust between the user and the hardware owner. Currently, the user cannot keep bitstream nor data protected from the hardware owner in an FPGA-as-a-service setting. This paper proposes a security model where the chip manufacturer takes the role of root-of-trust to remedy these security problems. We suggest that the chip manufacturer creates a Public Key Infrastructure (PKI), used for user bitstream protection and data encryption, on each device. The chip manufacturer, rather than the hardware owner, also controls certain security-related peripherals. This allows the user to take control over a predefined part of the programmable logic and set up a protected enclave area. Hence, all user data can be provided in encrypted form and only be revealed inside the enclave area. In addition, our model enables secure and concurrent multi-tenant usage of remote FPGAs. To also consider the needs of the hardware owner, our solution includes bitstream certification and affirming that uploaded bitstreams have been vetted against maliciousness.
The National Airspace System (NAS), as a portion of the US' transportation system, has not yet begun to model or adopt integration of Artificial Intelligence (AI) technology. However, users of the NAS, i.e., Air transport operators, UAS operators, etc. are beginning to use this technology throughout their operations. At issue within the broader aviation marketplace, is the continued search for a solution set to the persistent daily delays and schedule perturbations that occur within the NAS. Despite billions invested through the NAS Modernization Program, the delays persist in the face of reduced demand for commercial routings. Every delay represents an economic loss to commercial transport operators, passengers, freighters, and any business depending on the transportation performance. Therefore, the FAA needs to begin to address from an advanced concepts perspective, what this wave of new technology will affect as it is brought to bear on various operations performance parameters, including safety, security, efficiency, and resiliency solution sets. This paper is the first in a series of papers we are developing to explore the application of AI in the National Airspace System (NAS). This first paper is meant to get everyone in the aviation community on the same page, a primer if you will, to start the technical discussions. This paper will define AI; the capabilities associated with AI; current use cases within the aviation ecosystem; and how to prepare for insertion of AI in the NAS. The next series of papers will look at NAS Operations Theory utilizing AI capabilities and eventually leading to a future intelligent NAS (iNAS) environment.
The need for increased surveillance due to increase in flight volume in remote or oceanic regions outside the range of traditional radar coverage has been fulfilled by the advent of space-based Automatic Dependent Surveillance — Broadcast (ADS-B) Surveillance systems. ADS-B systems have the capability of providing air traffic controllers with highly accurate real-time flight data. ADS-B is dependent on digital communications between aircraft and ground stations of the air route traffic control center (ARTCC); however these communications are not secured. Anyone with the appropriate capabilities and equipment can interrogate the signal and transmit their own false data; this is known as spoofing. The possibility of this type of attacks decreases the situational awareness of United States airspace. The purpose of this project is to design a secure transmission framework that prevents ADS-B signals from being spoofed. Three alternative methods of securing ADS-B signals are evaluated: hashing, symmetric encryption, and asymmetric encryption. Security strength of the design alternatives is determined from research. Feasibility criteria are determined by comparative analysis of alternatives. Economic implications and possible collision risk is determined from simulations that model the United State airspace over the Gulf of Mexico and part of the airspace under attack respectively. The ultimate goal of the project is to show that if ADS-B signals can be secured, the situational awareness can improve and the ARTCC can use information from this surveillance system to decrease the separation between aircraft and ultimately maximize the use of the United States airspace.
We propose a general approach to construct cryptographic significant Boolean functions of (r + 1)m variables based on the additive decomposition F2rm × F2m of the finite field F2(r+1)m, where r ≥ 1 is odd and m ≥ 3. A class of unbalanced functions is constructed first via this approach, which coincides with a variant of the unbalanced class of generalized Tu-Deng functions in the case r = 1. Functions belonging to this class have high algebraic degree, but their algebraic immunity does not exceed m, which is impossible to be optimal when r > 1. By modifying these unbalanced functions, we obtain a class of balanced functions which have optimal algebraic degree and high nonlinearity (shown by a lower bound we prove). These functions have optimal algebraic immunity provided a combinatorial conjecture on binary strings which generalizes the Tu-Deng conjecture is true. Computer investigations show that, at least for small values of number of variables, functions from this class also behave well against fast algebraic attacks.
We propose a general approach to construct cryptographic significant Boolean functions of (r + 1)m variables based on the additive decomposition F2rm × F2m of the finite field F2(r+1)m, where r ≥ 1 is odd and m ≥ 3. A class of unbalanced functions is constructed first via this approach, which coincides with a variant of the unbalanced class of generalized Tu-Deng functions in the case r = 1. Functions belonging to this class have high algebraic degree, but their algebraic immunity does not exceed m, which is impossible to be optimal when r > 1. By modifying these unbalanced functions, we obtain a class of balanced functions which have optimal algebraic degree and high nonlinearity (shown by a lower bound we prove). These functions have optimal algebraic immunity provided a combinatorial conjecture on binary strings which generalizes the Tu-Deng conjecture is true. Computer investigations show that, at least for small values of number of variables, functions from this class also behave well against fast algebraic attacks.